Overview

Dataset statistics

Number of variables43
Number of observations799380
Missing cells62
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1006.1 MiB
Average record size in memory1.3 KiB

Variable types

CAT22
NUM16
UNSUPPORTED3
DATE2

Warnings

VI has constant value "799380" Constant
QU has constant value "799380" Constant
FOLIO has a high cardinality: 366061 distinct values High cardinality
SUB has a high cardinality: 8630 distinct values High cardinality
SITE_ADDR has a high cardinality: 365612 distinct values High cardinality
SITE_CITY has a high cardinality: 66 distinct values High cardinality
SITE_ZIP has a high cardinality: 2120 distinct values High cardinality
SD1 has a high cardinality: 169 distinct values High cardinality
NBHC has a high cardinality: 313 distinct values High cardinality
BLOCK_NUM has a high cardinality: 885 distinct values High cardinality
LOT_NUM has a high cardinality: 19038 distinct values High cardinality
BLDG is highly correlated with JUST and 1 other fieldsHigh correlation
JUST is highly correlated with BLDG and 2 other fieldsHigh correlation
ASD_VAL is highly correlated with JUST and 2 other fieldsHigh correlation
TAX_VAL is highly correlated with JUST and 1 other fieldsHigh correlation
REGION is highly correlated with MARKET_AREA_CDHigh correlation
MARKET_AREA_CD is highly correlated with REGIONHigh correlation
ACREAGE is highly skewed (γ1 = 46.57661552) Skewed
FOLIO is uniformly distributed Uniform
SITE_ADDR is uniformly distributed Uniform
df_index has unique values Unique
ACT is an unsupported type, check if it needs cleaning or further analysis Unsupported
EFF is an unsupported type, check if it needs cleaning or further analysis Unsupported
BASE is an unsupported type, check if it needs cleaning or further analysis Unsupported
tBLDGS has 166987 (20.9%) zeros Zeros
EXF has 304860 (38.1%) zeros Zeros
AGE has 165687 (20.7%) zeros Zeros

Reproduction

Analysis started2022-05-28 15:23:06.763688
Analysis finished2022-05-28 15:27:57.671766
Duration4 minutes and 50.91 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct799380
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean974173.565
Minimum8
Maximum2047226
Zeros0
Zeros (%)0.0%
Memory size6.1 MiB
2022-05-28T11:27:58.130463image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile93321.9
Q1439599.75
median963247
Q31490838.25
95-th percentile1944786.2
Maximum2047226
Range2047218
Interquartile range (IQR)1051238.5

Descriptive statistics

Standard deviation592102.3056
Coefficient of variation (CV)0.6077996026
Kurtosis-1.189629105
Mean974173.565
Median Absolute Deviation (MAD)525606.5
Skewness0.09635878238
Sum7.787348644e+11
Variance3.505851403e+11
MonotocityStrictly increasing
2022-05-28T11:27:58.292431image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
81< 0.1%
 
12840351< 0.1%
 
12839941< 0.1%
 
12839961< 0.1%
 
12839981< 0.1%
 
12839991< 0.1%
 
12840011< 0.1%
 
12840031< 0.1%
 
12840041< 0.1%
 
12840061< 0.1%
 
12840081< 0.1%
 
12840101< 0.1%
 
12840121< 0.1%
 
12840141< 0.1%
 
12840171< 0.1%
 
12840191< 0.1%
 
12840211< 0.1%
 
12840231< 0.1%
 
12840251< 0.1%
 
12840261< 0.1%
 
12840281< 0.1%
 
12840301< 0.1%
 
12840311< 0.1%
 
12839931< 0.1%
 
12839911< 0.1%
 
Other values (799355)799355> 99.9%
 
ValueCountFrequency (%) 
81< 0.1%
 
111< 0.1%
 
141< 0.1%
 
201< 0.1%
 
211< 0.1%
 
231< 0.1%
 
241< 0.1%
 
251< 0.1%
 
261< 0.1%
 
271< 0.1%
 
ValueCountFrequency (%) 
20472261< 0.1%
 
20472231< 0.1%
 
20472201< 0.1%
 
20472141< 0.1%
 
20472131< 0.1%
 
20472111< 0.1%
 
20472081< 0.1%
 
20472011< 0.1%
 
20471901< 0.1%
 
20471881< 0.1%
 

FOLIO
Categorical

HIGH CARDINALITY
UNIFORM

Distinct366061
Distinct (%)45.8%
Missing0
Missing (%)0.0%
Memory size6.1 MiB
1488210176
 
11
1219430000
 
11
0045299115
 
11
0036808582
 
11
0723690000
 
10
Other values (366056)
799326 
ValueCountFrequency (%) 
148821017611< 0.1%
 
121943000011< 0.1%
 
004529911511< 0.1%
 
003680858211< 0.1%
 
072369000010< 0.1%
 
149926010010< 0.1%
 
157947000010< 0.1%
 
126415000010< 0.1%
 
067476000010< 0.1%
 
018959625810< 0.1%
 
074742052210< 0.1%
 
019030186610< 0.1%
 
146209000010< 0.1%
 
180030000010< 0.1%
 
186317502610< 0.1%
 
128977713010< 0.1%
 
016123751210< 0.1%
 
120369000010< 0.1%
 
036675504610< 0.1%
 
054952341210< 0.1%
 
057472360810< 0.1%
 
005231715610< 0.1%
 
148172010010< 0.1%
 
142577009010< 0.1%
 
027554762210< 0.1%
 
Other values (366036)799126> 99.9%
 
2022-05-28T11:27:59.602319image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique141211 ?
Unique (%)17.7%
2022-05-28T11:27:59.763622image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length10
Median length10
Mean length10
Min length10

Overview of Unicode Properties

Unique unicode characters10
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
0230574028.8%
 
17729299.7%
 
27268039.1%
 
46540178.2%
 
56514448.1%
 
76405138.0%
 
66091527.6%
 
85967377.5%
 
35900607.4%
 
94464055.6%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number7993800100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
0230574028.8%
 
17729299.7%
 
27268039.1%
 
46540178.2%
 
56514448.1%
 
76405138.0%
 
66091527.6%
 
85967377.5%
 
35900607.4%
 
94464055.6%
 

Most occurring scripts

ValueCountFrequency (%) 
Common7993800100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
0230574028.8%
 
17729299.7%
 
27268039.1%
 
46540178.2%
 
56514448.1%
 
76405138.0%
 
66091527.6%
 
85967377.5%
 
35900607.4%
 
94464055.6%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII7993800100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
0230574028.8%
 
17729299.7%
 
27268039.1%
 
46540178.2%
 
56514448.1%
 
76405138.0%
 
66091527.6%
 
85967377.5%
 
35900607.4%
 
94464055.6%
 

DOR_CODE
Categorical

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.1 MiB
0100
613252 
0400
92549 
0106
72966 
0200
 
11848
0800
 
7953
Other values (3)
 
812
ValueCountFrequency (%) 
010061325276.7%
 
04009254911.6%
 
0106729669.1%
 
0200118481.5%
 
080079531.0%
 
04086750.1%
 
0801102< 0.1%
 
010235< 0.1%
 
2022-05-28T11:27:59.913733image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-05-28T11:28:00.027820image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:28:00.150404image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length4
Median length4
Mean length4
Min length4

Overview of Unicode Properties

Unique unicode characters6
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
0232436272.7%
 
168635521.5%
 
4932242.9%
 
6729662.3%
 
2118830.4%
 
887300.3%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number3197520100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
0232436272.7%
 
168635521.5%
 
4932242.9%
 
6729662.3%
 
2118830.4%
 
887300.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Common3197520100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
0232436272.7%
 
168635521.5%
 
4932242.9%
 
6729662.3%
 
2118830.4%
 
887300.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII3197520100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
0232436272.7%
 
168635521.5%
 
4932242.9%
 
6729662.3%
 
2118830.4%
 
887300.3%
 

S_DATE
Date

Distinct8090
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size6.1 MiB
Minimum1980-01-01 00:00:00
Maximum2022-01-28 00:00:00
2022-05-28T11:28:00.314833image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:28:00.540697image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

VI
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.1 MiB
I
799380 
ValueCountFrequency (%) 
I799380100.0%
 
2022-05-28T11:28:00.716845image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-05-28T11:28:00.785597image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:28:00.919474image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters1
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
I799380100.0%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter799380100.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
I799380100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin799380100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
I799380100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII799380100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
I799380100.0%
 

QU
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.1 MiB
Q
799380 
ValueCountFrequency (%) 
Q799380100.0%
 
2022-05-28T11:28:01.102338image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-05-28T11:28:01.188266image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:28:01.288955image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters1
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
Q799380100.0%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter799380100.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
Q799380100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin799380100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
Q799380100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII799380100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
Q799380100.0%
 

REA_CD
Categorical

Distinct26
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.1 MiB
01
503916 
02
250456 
2A
 
15419
2B
 
15413
00
 
13472
Other values (21)
 
704
ValueCountFrequency (%) 
0150391663.0%
 
0225045631.3%
 
2A154191.9%
 
2B154131.9%
 
00134721.7%
 
38204< 0.1%
 
3C171< 0.1%
 
0592< 0.1%
 
3A58< 0.1%
 
3244< 0.1%
 
3D31< 0.1%
 
3720< 0.1%
 
1819< 0.1%
 
1217< 0.1%
 
3011< 0.1%
 
1111< 0.1%
 
3B6< 0.1%
 
206< 0.1%
 
193< 0.1%
 
352< 0.1%
 
342< 0.1%
 
142< 0.1%
 
982< 0.1%
 
211< 0.1%
 
131< 0.1%
 
2022-05-28T11:28:01.403647image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique3 ?
Unique (%)< 0.1%
2022-05-28T11:28:01.519961image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length2
Min length2

Overview of Unicode Properties

Unique unicode characters13
Unique unicode categories2 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
078142648.9%
 
150398131.5%
 
228135617.6%
 
A154771.0%
 
B154191.0%
 
3550< 0.1%
 
8225< 0.1%
 
C171< 0.1%
 
594< 0.1%
 
D31< 0.1%
 
720< 0.1%
 
95< 0.1%
 
45< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number156766298.1%
 
Uppercase Letter310981.9%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
078142649.8%
 
150398132.1%
 
228135617.9%
 
3550< 0.1%
 
8225< 0.1%
 
594< 0.1%
 
720< 0.1%
 
95< 0.1%
 
45< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A1547749.8%
 
B1541949.6%
 
C1710.5%
 
D310.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Common156766298.1%
 
Latin310981.9%
 

Most frequent Common characters

ValueCountFrequency (%) 
078142649.8%
 
150398132.1%
 
228135617.9%
 
3550< 0.1%
 
8225< 0.1%
 
594< 0.1%
 
720< 0.1%
 
95< 0.1%
 
45< 0.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
A1547749.8%
 
B1541949.6%
 
C1710.5%
 
D310.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1598760100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
078142648.9%
 
150398131.5%
 
228135617.6%
 
A154771.0%
 
B154191.0%
 
3550< 0.1%
 
8225< 0.1%
 
C171< 0.1%
 
594< 0.1%
 
D31< 0.1%
 
720< 0.1%
 
95< 0.1%
 
45< 0.1%
 

S_AMT
Real number (ℝ≥0)

Distinct10878
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean190000.9628
Minimum1113
Maximum22519500
Zeros0
Zeros (%)0.0%
Memory size6.1 MiB
2022-05-28T11:28:01.720795image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1113
5-th percentile39000
Q182500
median148000
Q3239000
95-th percentile460000
Maximum22519500
Range22518387
Interquartile range (IQR)156500

Descriptive statistics

Standard deviation197917.0903
Coefficient of variation (CV)1.041663618
Kurtosis391.5007362
Mean190000.9628
Median Absolute Deviation (MAD)73000
Skewness10.46525629
Sum1.518829696e+11
Variance3.917117463e+10
MonotocityNot monotonic
2022-05-28T11:28:01.914514image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
12500047830.6%
 
15000046090.6%
 
7500044780.6%
 
6500044730.6%
 
13500042960.5%
 
8500042880.5%
 
17500041870.5%
 
12000041370.5%
 
11000040870.5%
 
13000040540.5%
 
16500040490.5%
 
11500040460.5%
 
14000040000.5%
 
16000039870.5%
 
14500039080.5%
 
5500038510.5%
 
20000038450.5%
 
8000038130.5%
 
6000037960.5%
 
5000037610.5%
 
9000037170.5%
 
15500037170.5%
 
17000036670.5%
 
18000036360.5%
 
10000036330.5%
 
Other values (10853)69856287.4%
 
ValueCountFrequency (%) 
11131< 0.1%
 
12004< 0.1%
 
13001< 0.1%
 
14001< 0.1%
 
15007< 0.1%
 
15241< 0.1%
 
16002< 0.1%
 
17901< 0.1%
 
18002< 0.1%
 
19001< 0.1%
 
ValueCountFrequency (%) 
225195001< 0.1%
 
128000001< 0.1%
 
102636001< 0.1%
 
96000001< 0.1%
 
91000001< 0.1%
 
84296001< 0.1%
 
78000001< 0.1%
 
75000002< 0.1%
 
69000001< 0.1%
 
68686001< 0.1%
 

SUB
Categorical

HIGH CARDINALITY

Distinct8630
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size6.1 MiB
ZZZ
 
29375
3U4
 
2654
509
 
2563
42J
 
1900
3TP
 
1746
Other values (8625)
761142 
ValueCountFrequency (%) 
ZZZ293753.7%
 
3U426540.3%
 
50925630.3%
 
42J19000.2%
 
3TP17460.2%
 
45M16160.2%
 
45414100.2%
 
3LA13690.2%
 
10413300.2%
 
3TR13010.2%
 
4PQ10900.1%
 
82010160.1%
 
88J10060.1%
 
3T79990.1%
 
0BJ9880.1%
 
1TM9840.1%
 
89N9770.1%
 
36C9730.1%
 
8639640.1%
 
9D79600.1%
 
98M9490.1%
 
3D69450.1%
 
82P9350.1%
 
09K9140.1%
 
82X8810.1%
 
Other values (8605)73953592.5%
 
2022-05-28T11:28:02.131012image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique328 ?
Unique (%)< 0.1%
2022-05-28T11:28:02.258194image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3

Overview of Unicode Properties

Unique unicode characters36
Unique unicode categories2 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
01781787.4%
 
21629826.8%
 
31609236.7%
 
11482856.2%
 
41350115.6%
 
51346225.6%
 
Z1230585.1%
 
91088224.5%
 
8887263.7%
 
7865223.6%
 
6814513.4%
 
A592942.5%
 
B534882.2%
 
P508072.1%
 
X440211.8%
 
U435531.8%
 
T434511.8%
 
W428761.8%
 
V412981.7%
 
C404351.7%
 
Q393931.6%
 
J386911.6%
 
F385191.6%
 
E379681.6%
 
Y379281.6%
 
Other values (11)37783815.8%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number128552253.6%
 
Uppercase Letter111261846.4%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
017817813.9%
 
216298212.7%
 
316092312.5%
 
114828511.5%
 
413501110.5%
 
513462210.5%
 
91088228.5%
 
8887266.9%
 
7865226.7%
 
6814516.3%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
Z12305811.1%
 
A592945.3%
 
B534884.8%
 
P508074.6%
 
X440214.0%
 
U435533.9%
 
T434513.9%
 
W428763.9%
 
V412983.7%
 
C404353.6%
 
Q393933.5%
 
J386913.5%
 
F385193.5%
 
E379683.4%
 
Y379283.4%
 
D375403.4%
 
I369103.3%
 
H364443.3%
 
R356693.2%
 
S349493.1%
 
L335753.0%
 
M330673.0%
 
O330483.0%
 
G327592.9%
 
N324782.9%
 

Most occurring scripts

ValueCountFrequency (%) 
Common128552253.6%
 
Latin111261846.4%
 

Most frequent Common characters

ValueCountFrequency (%) 
017817813.9%
 
216298212.7%
 
316092312.5%
 
114828511.5%
 
413501110.5%
 
513462210.5%
 
91088228.5%
 
8887266.9%
 
7865226.7%
 
6814516.3%
 

Most frequent Latin characters

ValueCountFrequency (%) 
Z12305811.1%
 
A592945.3%
 
B534884.8%
 
P508074.6%
 
X440214.0%
 
U435533.9%
 
T434513.9%
 
W428763.9%
 
V412983.7%
 
C404353.6%
 
Q393933.5%
 
J386913.5%
 
F385193.5%
 
E379683.4%
 
Y379283.4%
 
D375403.4%
 
I369103.3%
 
H364443.3%
 
R356693.2%
 
S349493.1%
 
L335753.0%
 
M330673.0%
 
O330483.0%
 
G327592.9%
 
N324782.9%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII2398140100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
01781787.4%
 
21629826.8%
 
31609236.7%
 
11482856.2%
 
41350115.6%
 
51346225.6%
 
Z1230585.1%
 
91088224.5%
 
8887263.7%
 
7865223.6%
 
6814513.4%
 
A592942.5%
 
B534882.2%
 
P508072.1%
 
X440211.8%
 
U435531.8%
 
T434511.8%
 
W428761.8%
 
V412981.7%
 
C404351.7%
 
Q393931.6%
 
J386911.6%
 
F385191.6%
 
E379681.6%
 
Y379281.6%
 
Other values (11)37783815.8%
 

S_TYPE
Categorical

Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.1 MiB
WD
787934 
TR
 
6418
AG
 
1619
AD
 
1055
FD
 
675
Other values (14)
 
1679
ValueCountFrequency (%) 
WD78793498.6%
 
TR64180.8%
 
AG16190.2%
 
AD10550.1%
 
FD6750.1%
 
QC6150.1%
 
CT4240.1%
 
00187< 0.1%
 
DD108< 0.1%
 
PR97< 0.1%
 
GD89< 0.1%
 
AS66< 0.1%
 
CD36< 0.1%
 
TD19< 0.1%
 
ED17< 0.1%
 
MD17< 0.1%
 
WQ2< 0.1%
 
WS1< 0.1%
 
SD1< 0.1%
 
2022-05-28T11:28:02.388560image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique2 ?
Unique (%)< 0.1%
2022-05-28T11:28:02.513193image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length2
Min length2

Overview of Unicode Properties

Unique unicode characters14
Unique unicode categories2 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
D79005949.4%
 
W78793749.3%
 
T68610.4%
 
R65150.4%
 
A27400.2%
 
G17080.1%
 
C10750.1%
 
F675< 0.1%
 
Q617< 0.1%
 
0374< 0.1%
 
P97< 0.1%
 
S68< 0.1%
 
E17< 0.1%
 
M17< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter1598386> 99.9%
 
Decimal Number374< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
D79005949.4%
 
W78793749.3%
 
T68610.4%
 
R65150.4%
 
A27400.2%
 
G17080.1%
 
C10750.1%
 
F675< 0.1%
 
Q617< 0.1%
 
P97< 0.1%
 
S68< 0.1%
 
E17< 0.1%
 
M17< 0.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
0374100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin1598386> 99.9%
 
Common374< 0.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
D79005949.4%
 
W78793749.3%
 
T68610.4%
 
R65150.4%
 
A27400.2%
 
G17080.1%
 
C10750.1%
 
F675< 0.1%
 
Q617< 0.1%
 
P97< 0.1%
 
S68< 0.1%
 
E17< 0.1%
 
M17< 0.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
0374100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1598760100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
D79005949.4%
 
W78793749.3%
 
T68610.4%
 
R65150.4%
 
A27400.2%
 
G17080.1%
 
C10750.1%
 
F675< 0.1%
 
Q617< 0.1%
 
0374< 0.1%
 
P97< 0.1%
 
S68< 0.1%
 
E17< 0.1%
 
M17< 0.1%
 
Distinct6153
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size6.1 MiB
Minimum1901-12-01 00:00:00
Maximum2022-01-19 00:00:00
2022-05-28T11:28:02.641662image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:28:02.789877image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

SITE_ADDR
Categorical

HIGH CARDINALITY
UNIFORM

Distinct365612
Distinct (%)45.7%
Missing55
Missing (%)< 0.1%
Memory size6.1 MiB
611 DESTINY DR
 
233
4201 BAYSHORE BLVD
 
132
2001 E 2ND AVE
 
104
3507 BAYSHORE BLVD
 
76
3119 W DELEON ST
 
27
Other values (365607)
798753 
ValueCountFrequency (%) 
611 DESTINY DR233< 0.1%
 
4201 BAYSHORE BLVD132< 0.1%
 
2001 E 2ND AVE104< 0.1%
 
3507 BAYSHORE BLVD76< 0.1%
 
3119 W DELEON ST27< 0.1%
 
1002 CHANNELSIDE DR22< 0.1%
 
019< 0.1%
 
902 S ROME AVE12< 0.1%
 
1022 BELLASOL WAY12< 0.1%
 
5026 W DICKENS AVE11< 0.1%
 
7130 WATERSIDE DR11< 0.1%
 
12415 MONDRAGON DR11< 0.1%
 
8523 J R MANOR DR11< 0.1%
 
3109 W HAWTHORNE RD10< 0.1%
 
13554 AVISTA DR10< 0.1%
 
1604 E NOME ST10< 0.1%
 
4101 W MORRISON AVE10< 0.1%
 
301 KNOTTWOOD CT10< 0.1%
 
109 MELANIE LN10< 0.1%
 
136 BUTLER RD10< 0.1%
 
3516 W OBISPO ST10< 0.1%
 
1026 BELLASOL WAY10< 0.1%
 
8601 N 39TH ST10< 0.1%
 
1912 GRAND CYPRESS LN10< 0.1%
 
1002 E SEWARD ST10< 0.1%
 
Other values (365587)79852499.9%
 
(Missing)55< 0.1%
 
2022-05-28T11:28:04.551219image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique140979 ?
Unique (%)17.6%
2022-05-28T11:28:04.830440image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length40
Median length18
Mean length18.42768771
Min length1

Overview of Unicode Properties

Unique unicode characters57
Unique unicode categories8 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
218398414.8%
 
R9782416.6%
 
E9266246.3%
 
A8223515.6%
 
18081455.5%
 
D6249344.2%
 
L6201874.2%
 
N5765013.9%
 
05412543.7%
 
O5385423.7%
 
S5162453.5%
 
T5101333.5%
 
I4621593.1%
 
24469383.0%
 
C3420012.3%
 
33310842.2%
 
43056762.1%
 
W2662371.8%
 
52579901.8%
 
H2427391.6%
 
62329771.6%
 
V2317911.6%
 
82137561.5%
 
B2136941.5%
 
72101871.4%
 
Other values (32)13263559.0%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter900647861.1%
 
Decimal Number353828724.0%
 
Space Separator218398414.8%
 
Dash Punctuation933< 0.1%
 
Other Punctuation770< 0.1%
 
Lowercase Letter271< 0.1%
 
Modifier Symbol1< 0.1%
 
Open Punctuation1< 0.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
180814522.8%
 
054125415.3%
 
244693812.6%
 
33310849.4%
 
43056768.6%
 
52579907.3%
 
62329776.6%
 
82137566.0%
 
72101875.9%
 
91902805.4%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
2183984100.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
R97824110.9%
 
E92662410.3%
 
A8223519.1%
 
D6249346.9%
 
L6201876.9%
 
N5765016.4%
 
O5385426.0%
 
S5162455.7%
 
T5101335.7%
 
I4621595.1%
 
C3420013.8%
 
W2662373.0%
 
H2427392.7%
 
V2317912.6%
 
B2136942.4%
 
P1924482.1%
 
G1841842.0%
 
M1695191.9%
 
Y1684541.9%
 
K1580301.8%
 
U1362351.5%
 
F869941.0%
 
J126620.1%
 
X104320.1%
 
Z92080.1%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-933100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
/47661.8%
 
#22829.6%
 
&577.4%
 
.81.0%
 
,10.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n12345.4%
 
a6624.4%
 
s165.9%
 
r134.8%
 
i124.4%
 
e114.1%
 
d103.7%
 
o103.7%
 
t72.6%
 
u10.4%
 
h10.4%
 
g10.4%
 

Most frequent Modifier Symbol characters

ValueCountFrequency (%) 
`1100.0%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
[1100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin900674961.1%
 
Common572397638.9%
 

Most frequent Common characters

ValueCountFrequency (%) 
218398438.2%
 
180814514.1%
 
05412549.5%
 
24469387.8%
 
33310845.8%
 
43056765.3%
 
52579904.5%
 
62329774.1%
 
82137563.7%
 
72101873.7%
 
91902803.3%
 
-933< 0.1%
 
/476< 0.1%
 
#228< 0.1%
 
&57< 0.1%
 
.8< 0.1%
 
,1< 0.1%
 
`1< 0.1%
 
[1< 0.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
R97824110.9%
 
E92662410.3%
 
A8223519.1%
 
D6249346.9%
 
L6201876.9%
 
N5765016.4%
 
O5385426.0%
 
S5162455.7%
 
T5101335.7%
 
I4621595.1%
 
C3420013.8%
 
W2662373.0%
 
H2427392.7%
 
V2317912.6%
 
B2136942.4%
 
P1924482.1%
 
G1841842.0%
 
M1695191.9%
 
Y1684541.9%
 
K1580301.8%
 
U1362351.5%
 
F869941.0%
 
J126620.1%
 
X104320.1%
 
Z92080.1%
 
Other values (13)62040.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII14730725100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
218398414.8%
 
R9782416.6%
 
E9266246.3%
 
A8223515.6%
 
18081455.5%
 
D6249344.2%
 
L6201874.2%
 
N5765013.9%
 
05412543.7%
 
O5385423.7%
 
S5162453.5%
 
T5101333.5%
 
I4621593.1%
 
24469383.0%
 
C3420012.3%
 
33310842.2%
 
43056762.1%
 
W2662371.8%
 
52579901.8%
 
H2427391.6%
 
62329771.6%
 
V2317911.6%
 
82137561.5%
 
B2136941.5%
 
72101871.4%
 
Other values (32)13263559.0%
 

SITE_CITY
Categorical

HIGH CARDINALITY

Distinct66
Distinct (%)< 0.1%
Missing7
Missing (%)< 0.1%
Memory size6.1 MiB
TAMPA
425012 
RIVERVIEW
71689 
BRANDON
50923 
VALRICO
46190 
SUN CITY CENTER
 
32840
Other values (61)
172719 
ValueCountFrequency (%) 
TAMPA42501253.2%
 
RIVERVIEW716899.0%
 
BRANDON509236.4%
 
VALRICO461905.8%
 
SUN CITY CENTER328404.1%
 
PLANT CITY279523.5%
 
LUTZ266173.3%
 
APOLLO BEACH186232.3%
 
RUSKIN181952.3%
 
TEMPLE TERRACE162402.0%
 
LITHIA153991.9%
 
SEFFNER147581.8%
 
ODESSA100491.3%
 
WIMAUMA93471.2%
 
GIBSONTON75390.9%
 
DOVER44760.6%
 
THONOTOSASSA26390.3%
 
Tampa396< 0.1%
 
Unincorporated204< 0.1%
 
LAKELAND114< 0.1%
 
Plant City44< 0.1%
 
Temple Terrace34< 0.1%
 
ZEPHYRHILLS24< 0.1%
 
WIMAUAM6< 0.1%
 
MULBERRY4< 0.1%
 
Other values (41)59< 0.1%
 
(Missing)7< 0.1%
 
2022-05-28T11:28:05.189905image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique28 ?
Unique (%)< 0.1%
2022-05-28T11:28:05.420769image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length26
Median length5
Mean length6.679626711
Min length3

Overview of Unicode Properties

Unique unicode characters46
Unique unicode categories5 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
A107829320.2%
 
T63439511.9%
 
P4879119.1%
 
M4600008.6%
 
R3433206.4%
 
E3368636.3%
 
I3163275.9%
 
N2462884.6%
 
V1940723.6%
 
C1747523.3%
 
O1719133.2%
 
L1699473.2%
 
1286032.4%
 
S1013871.9%
 
U872371.6%
 
W810651.5%
 
B770951.4%
 
D655681.2%
 
Y608291.1%
 
H367230.7%
 
F295180.6%
 
Z266410.5%
 
K183100.3%
 
G75480.1%
 
a1081< 0.1%
 
Other values (21)38740.1%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter520600797.5%
 
Space Separator1286032.4%
 
Lowercase Letter49390.1%
 
Decimal Number9< 0.1%
 
Modifier Symbol2< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A107829320.7%
 
T63439512.2%
 
P4879119.4%
 
M4600008.8%
 
R3433206.6%
 
E3368636.5%
 
I3163276.1%
 
N2462884.7%
 
V1940723.7%
 
C1747523.4%
 
O1719133.3%
 
L1699473.3%
 
S1013871.9%
 
U872371.7%
 
W810651.6%
 
B770951.5%
 
D655681.3%
 
Y608291.2%
 
H367230.7%
 
F295180.6%
 
Z266410.5%
 
K183100.4%
 
G75480.1%
 
Q5< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a108121.9%
 
p63412.8%
 
r4769.6%
 
n4669.4%
 
m4308.7%
 
o4088.3%
 
e3406.9%
 
t2925.9%
 
i2485.0%
 
c2384.8%
 
d2044.1%
 
l781.6%
 
y440.9%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
128603100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
3333.3%
 
5111.1%
 
6111.1%
 
9111.1%
 
8111.1%
 
2111.1%
 
0111.1%
 

Most frequent Modifier Symbol characters

ValueCountFrequency (%) 
`2100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin521094697.6%
 
Common1286142.4%
 

Most frequent Latin characters

ValueCountFrequency (%) 
A107829320.7%
 
T63439512.2%
 
P4879119.4%
 
M4600008.8%
 
R3433206.6%
 
E3368636.5%
 
I3163276.1%
 
N2462884.7%
 
V1940723.7%
 
C1747523.4%
 
O1719133.3%
 
L1699473.3%
 
S1013871.9%
 
U872371.7%
 
W810651.6%
 
B770951.5%
 
D655681.3%
 
Y608291.2%
 
H367230.7%
 
F295180.6%
 
Z266410.5%
 
K183100.4%
 
G75480.1%
 
a1081< 0.1%
 
p634< 0.1%
 
Other values (12)32290.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
128603> 99.9%
 
33< 0.1%
 
`2< 0.1%
 
51< 0.1%
 
61< 0.1%
 
91< 0.1%
 
81< 0.1%
 
21< 0.1%
 
01< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII5339560100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
A107829320.2%
 
T63439511.9%
 
P4879119.1%
 
M4600008.6%
 
R3433206.4%
 
E3368636.3%
 
I3163275.9%
 
N2462884.6%
 
V1940723.6%
 
C1747523.3%
 
O1719133.2%
 
L1699473.2%
 
1286032.4%
 
S1013871.9%
 
U872371.6%
 
W810651.5%
 
B770951.4%
 
D655681.2%
 
Y608291.1%
 
H367230.7%
 
F295180.6%
 
Z266410.5%
 
K183100.3%
 
G75480.1%
 
a1081< 0.1%
 
Other values (21)38740.1%
 

SITE_ZIP
Categorical

HIGH CARDINALITY

Distinct2120
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.1 MiB
33647
 
41660
33573
 
36279
33624
 
35007
33511
 
32442
33578
 
30377
Other values (2115)
623615 
ValueCountFrequency (%) 
33647416605.2%
 
33573362794.5%
 
33624350074.4%
 
33511324424.1%
 
33578303773.8%
 
33615285903.6%
 
33611248713.1%
 
33604240113.0%
 
33579237323.0%
 
33596236033.0%
 
33617228292.9%
 
33626228052.9%
 
33594220582.8%
 
33629220342.8%
 
33618193032.4%
 
33625188012.4%
 
33614186102.3%
 
33612185422.3%
 
33572183132.3%
 
33510180332.3%
 
33569159922.0%
 
33547152131.9%
 
33584145191.8%
 
33609141281.8%
 
33610140191.8%
 
Other values (2095)22360928.0%
 
2022-05-28T11:28:05.694844image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique762 ?
Unique (%)0.1%
2022-05-28T11:28:05.935894image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length10
Median length5
Mean length5.04016738
Min length5

Overview of Unicode Properties

Unique unicode characters12
Unique unicode categories3 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
3170809342.4%
 
657443514.3%
 
545428311.3%
 
12924497.3%
 
72323745.8%
 
42047415.1%
 
91631554.0%
 
21582113.9%
 
01394803.5%
 
8920912.3%
 
-96930.2%
 
_4< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number401931299.8%
 
Dash Punctuation96930.2%
 
Connector Punctuation4< 0.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
3170809342.5%
 
657443514.3%
 
545428311.3%
 
12924497.3%
 
72323745.8%
 
42047415.1%
 
91631554.1%
 
21582113.9%
 
01394803.5%
 
8920912.3%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-9693100.0%
 

Most frequent Connector Punctuation characters

ValueCountFrequency (%) 
_4100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common4029009100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
3170809342.4%
 
657443514.3%
 
545428311.3%
 
12924497.3%
 
72323745.8%
 
42047415.1%
 
91631554.0%
 
21582113.9%
 
01394803.5%
 
8920912.3%
 
-96930.2%
 
_4< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII4029009100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
3170809342.4%
 
657443514.3%
 
545428311.3%
 
12924497.3%
 
72323745.8%
 
42047415.1%
 
91631554.0%
 
21582113.9%
 
01394803.5%
 
8920912.3%
 
-96930.2%
 
_4< 0.1%
 

tBEDS
Real number (ℝ≥0)

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.096768746
Minimum0
Maximum24
Zeros3321
Zeros (%)0.4%
Memory size6.1 MiB
2022-05-28T11:28:06.142443image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q12
median3
Q34
95-th percentile5
Maximum24
Range24
Interquartile range (IQR)2

Descriptive statistics

Standard deviation0.9676617041
Coefficient of variation (CV)0.3124746417
Kurtosis1.122145041
Mean3.096768746
Median Absolute Deviation (MAD)1
Skewness0.167008142
Sum2475495
Variance0.9363691737
MonotocityNot monotonic
2022-05-28T11:28:06.487968image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%) 
333723542.2%
 
421015526.3%
 
217509021.9%
 
5408375.1%
 
1257743.2%
 
658960.7%
 
033210.4%
 
77650.1%
 
8146< 0.1%
 
939< 0.1%
 
2.523< 0.1%
 
3.523< 0.1%
 
1020< 0.1%
 
1118< 0.1%
 
0.310< 0.1%
 
1.59< 0.1%
 
138< 0.1%
 
127< 0.1%
 
5.53< 0.1%
 
241< 0.1%
 
ValueCountFrequency (%) 
033210.4%
 
0.310< 0.1%
 
1257743.2%
 
1.59< 0.1%
 
217509021.9%
 
2.523< 0.1%
 
333723542.2%
 
3.523< 0.1%
 
421015526.3%
 
5408375.1%
 
ValueCountFrequency (%) 
241< 0.1%
 
138< 0.1%
 
127< 0.1%
 
1118< 0.1%
 
1020< 0.1%
 
939< 0.1%
 
8146< 0.1%
 
77650.1%
 
658960.7%
 
5.53< 0.1%
 

tBATHS
Real number (ℝ≥0)

Distinct29
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.195919838
Minimum0
Maximum17
Zeros2613
Zeros (%)0.3%
Memory size6.1 MiB
2022-05-28T11:28:06.811842image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median2
Q32.5
95-th percentile3.5
Maximum17
Range17
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.755631731
Coefficient of variation (CV)0.3441071564
Kurtosis5.248610869
Mean2.195919838
Median Absolute Deviation (MAD)0
Skewness1.124793148
Sum1755374.4
Variance0.5709793129
MonotocityNot monotonic
2022-05-28T11:28:07.170344image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%) 
240338350.5%
 
2.512288015.4%
 
39807412.3%
 
19501911.9%
 
3.5250753.1%
 
1.5231712.9%
 
4165642.1%
 
4.563640.8%
 
528610.4%
 
026130.3%
 
5.517700.2%
 
66240.1%
 
6.54920.1%
 
7196< 0.1%
 
7.5132< 0.1%
 
847< 0.1%
 
8.535< 0.1%
 
923< 0.1%
 
10.514< 0.1%
 
119< 0.1%
 
9.56< 0.1%
 
0.56< 0.1%
 
105< 0.1%
 
14.54< 0.1%
 
11.54< 0.1%
 
Other values (4)9< 0.1%
 
ValueCountFrequency (%) 
026130.3%
 
0.56< 0.1%
 
19501911.9%
 
1.14< 0.1%
 
1.5231712.9%
 
240338350.5%
 
2.512288015.4%
 
39807412.3%
 
3.5250753.1%
 
4165642.1%
 
ValueCountFrequency (%) 
171< 0.1%
 
14.54< 0.1%
 
12.51< 0.1%
 
123< 0.1%
 
11.54< 0.1%
 
119< 0.1%
 
10.514< 0.1%
 
105< 0.1%
 
9.56< 0.1%
 
923< 0.1%
 

tSTORIES
Real number (ℝ≥0)

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.293691361
Minimum0
Maximum11
Zeros1490
Zeros (%)0.2%
Memory size6.1 MiB
2022-05-28T11:28:07.399863image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q32
95-th percentile2
Maximum11
Range11
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.4997404006
Coefficient of variation (CV)0.386290282
Kurtosis3.09329827
Mean1.293691361
Median Absolute Deviation (MAD)0
Skewness1.498667674
Sum1034151
Variance0.249740468
MonotocityNot monotonic
2022-05-28T11:28:07.684366image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%) 
157347671.7%
 
220882126.1%
 
398901.2%
 
1.536640.5%
 
415020.2%
 
014900.2%
 
2.5304< 0.1%
 
3.582< 0.1%
 
574< 0.1%
 
631< 0.1%
 
4.530< 0.1%
 
5.56< 0.1%
 
74< 0.1%
 
113< 0.1%
 
91< 0.1%
 
81< 0.1%
 
101< 0.1%
 
ValueCountFrequency (%) 
014900.2%
 
157347671.7%
 
1.536640.5%
 
220882126.1%
 
2.5304< 0.1%
 
398901.2%
 
3.582< 0.1%
 
415020.2%
 
4.530< 0.1%
 
574< 0.1%
 
ValueCountFrequency (%) 
113< 0.1%
 
101< 0.1%
 
91< 0.1%
 
81< 0.1%
 
74< 0.1%
 
631< 0.1%
 
5.56< 0.1%
 
574< 0.1%
 
4.530< 0.1%
 
415020.2%
 

tUNITS
Real number (ℝ≥0)

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.025342766
Minimum0
Maximum9
Zeros2598
Zeros (%)0.3%
Memory size6.1 MiB
2022-05-28T11:28:07.951713image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum9
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2476007742
Coefficient of variation (CV)0.2414809783
Kurtosis234.0352056
Mean1.025342766
Median Absolute Deviation (MAD)0
Skewness12.19445051
Sum819638.5
Variance0.06130614337
MonotocityNot monotonic
2022-05-28T11:28:08.164979image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%) 
178040397.6%
 
2131591.6%
 
025980.3%
 
417520.2%
 
310410.1%
 
5133< 0.1%
 
8113< 0.1%
 
6108< 0.1%
 
741< 0.1%
 
931< 0.1%
 
3.51< 0.1%
 
ValueCountFrequency (%) 
025980.3%
 
178040397.6%
 
2131591.6%
 
310410.1%
 
3.51< 0.1%
 
417520.2%
 
5133< 0.1%
 
6108< 0.1%
 
741< 0.1%
 
8113< 0.1%
 
ValueCountFrequency (%) 
931< 0.1%
 
8113< 0.1%
 
741< 0.1%
 
6108< 0.1%
 
5133< 0.1%
 
417520.2%
 
3.51< 0.1%
 
310410.1%
 
2131591.6%
 
178040397.6%
 

tBLDGS
Real number (ℝ≥0)

ZEROS

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8037216343
Minimum0
Maximum9
Zeros166987
Zeros (%)20.9%
Memory size6.1 MiB
2022-05-28T11:28:08.371992image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q31
95-th percentile1
Maximum9
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4305139744
Coefficient of variation (CV)0.5356505984
Kurtosis1.6761321
Mean0.8037216343
Median Absolute Deviation (MAD)0
Skewness-0.8747818601
Sum642479
Variance0.1853422821
MonotocityNot monotonic
2022-05-28T11:28:08.559037image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%) 
162300677.9%
 
016698720.9%
 
288531.1%
 
34170.1%
 
488< 0.1%
 
517< 0.1%
 
67< 0.1%
 
74< 0.1%
 
91< 0.1%
 
ValueCountFrequency (%) 
016698720.9%
 
162300677.9%
 
288531.1%
 
34170.1%
 
488< 0.1%
 
517< 0.1%
 
67< 0.1%
 
74< 0.1%
 
91< 0.1%
 
ValueCountFrequency (%) 
91< 0.1%
 
74< 0.1%
 
67< 0.1%
 
517< 0.1%
 
488< 0.1%
 
34170.1%
 
288531.1%
 
162300677.9%
 
016698720.9%
 

JUST
Real number (ℝ≥0)

HIGH CORRELATION

Distinct226334
Distinct (%)28.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean285392.8337
Minimum3650
Maximum16539559
Zeros0
Zeros (%)0.0%
Memory size6.1 MiB
2022-05-28T11:28:08.956161image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum3650
5-th percentile87835
Q1177064.25
median243093
Q3331585.5
95-th percentile593794.5
Maximum16539559
Range16535909
Interquartile range (IQR)154521.25

Descriptive statistics

Standard deviation225398.5716
Coefficient of variation (CV)0.7897835717
Kurtosis245.3899836
Mean285392.8337
Median Absolute Deviation (MAD)75014
Skewness8.830684037
Sum2.281373234e+11
Variance5.080451606e+10
MonotocityNot monotonic
2022-05-28T11:28:09.230213image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
74374398< 0.1%
 
71960373< 0.1%
 
153411369< 0.1%
 
56227362< 0.1%
 
89109361< 0.1%
 
65626347< 0.1%
 
73493335< 0.1%
 
102194317< 0.1%
 
73014302< 0.1%
 
161358294< 0.1%
 
66309287< 0.1%
 
68001285< 0.1%
 
103539278< 0.1%
 
55846277< 0.1%
 
118750274< 0.1%
 
60777274< 0.1%
 
146415233< 0.1%
 
77703230< 0.1%
 
82949228< 0.1%
 
30226227< 0.1%
 
80766225< 0.1%
 
47602222< 0.1%
 
47821219< 0.1%
 
45562218< 0.1%
 
111164218< 0.1%
 
Other values (226309)79222799.1%
 
ValueCountFrequency (%) 
365016< 0.1%
 
385022< 0.1%
 
400817< 0.1%
 
41848< 0.1%
 
42222< 0.1%
 
42663< 0.1%
 
443958< 0.1%
 
45811< 0.1%
 
47552< 0.1%
 
480054< 0.1%
 
ValueCountFrequency (%) 
165395593< 0.1%
 
122213251< 0.1%
 
109645312< 0.1%
 
95577754< 0.1%
 
93023244< 0.1%
 
87427943< 0.1%
 
76938661< 0.1%
 
75440332< 0.1%
 
75312531< 0.1%
 
74702912< 0.1%
 

LAND
Real number (ℝ≥0)

Distinct90608
Distinct (%)11.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76649.44226
Minimum75
Maximum8673113
Zeros0
Zeros (%)0.0%
Memory size6.1 MiB
2022-05-28T11:28:09.563348image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum75
5-th percentile100
Q139600
median63165
Q388906
95-th percentile200849.3
Maximum8673113
Range8673038
Interquartile range (IQR)49306

Descriptive statistics

Standard deviation95257.47075
Coefficient of variation (CV)1.242767957
Kurtosis489.9823977
Mean76649.44226
Median Absolute Deviation (MAD)24637
Skewness12.68188517
Sum6.127203116e+10
Variance9073985734
MonotocityNot monotonic
2022-05-28T11:28:09.898051image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1009315411.7%
 
21000016820.2%
 
8000016560.2%
 
3939014370.2%
 
5049013730.2%
 
6300013200.2%
 
5940012690.2%
 
20000011920.1%
 
3250011660.1%
 
6732010880.1%
 
12480010540.1%
 
4158010320.1%
 
586509830.1%
 
468009400.1%
 
550008790.1%
 
693008720.1%
 
699938610.1%
 
550808470.1%
 
583288220.1%
 
334757880.1%
 
328257670.1%
 
499957530.1%
 
720007300.1%
 
714007260.1%
 
727207230.1%
 
Other values (90583)68126685.2%
 
ValueCountFrequency (%) 
7537< 0.1%
 
1009315411.7%
 
4021< 0.1%
 
6527< 0.1%
 
6543< 0.1%
 
9992< 0.1%
 
11951< 0.1%
 
13174< 0.1%
 
13291< 0.1%
 
13422< 0.1%
 
ValueCountFrequency (%) 
86731132< 0.1%
 
65022031< 0.1%
 
64621573< 0.1%
 
62541061< 0.1%
 
49655034< 0.1%
 
47736002< 0.1%
 
42436391< 0.1%
 
40202303< 0.1%
 
37444902< 0.1%
 
36732381< 0.1%
 

BLDG
Real number (ℝ≥0)

HIGH CORRELATION

Distinct190197
Distinct (%)23.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean200986.4442
Minimum0
Maximum9840672
Zeros2649
Zeros (%)0.3%
Memory size6.1 MiB
2022-05-28T11:28:10.419124image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile68377
Q1129029
median172849.5
Q3232648
95-th percentile411329
Maximum9840672
Range9840672
Interquartile range (IQR)103619

Descriptive statistics

Standard deviation147538.3514
Coefficient of variation (CV)0.7340711558
Kurtosis185.343745
Mean200986.4442
Median Absolute Deviation (MAD)49909.5
Skewness7.728173867
Sum1.606645438e+11
Variance2.176756513e+10
MonotocityNot monotonic
2022-05-28T11:28:10.618649image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
026490.3%
 
74274398< 0.1%
 
71860375< 0.1%
 
153311369< 0.1%
 
56127362< 0.1%
 
89009356< 0.1%
 
63987347< 0.1%
 
73393335< 0.1%
 
102094317< 0.1%
 
161258300< 0.1%
 
72914299< 0.1%
 
66209287< 0.1%
 
67901285< 0.1%
 
103439285< 0.1%
 
118650279< 0.1%
 
54207277< 0.1%
 
60677274< 0.1%
 
131773235< 0.1%
 
77603234< 0.1%
 
80387229< 0.1%
 
30126228< 0.1%
 
79490225< 0.1%
 
46914222< 0.1%
 
47721220< 0.1%
 
111064219< 0.1%
 
Other values (190172)78977498.8%
 
ValueCountFrequency (%) 
026490.3%
 
5162< 0.1%
 
5233< 0.1%
 
6333< 0.1%
 
7172< 0.1%
 
8091< 0.1%
 
8102< 0.1%
 
8711< 0.1%
 
8792< 0.1%
 
9222< 0.1%
 
ValueCountFrequency (%) 
98406723< 0.1%
 
70642304< 0.1%
 
66046161< 0.1%
 
56157691< 0.1%
 
52846203< 0.1%
 
50675221< 0.1%
 
49170342< 0.1%
 
49070071< 0.1%
 
47062751< 0.1%
 
46725882< 0.1%
 

EXF
Real number (ℝ≥0)

ZEROS

Distinct33045
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7681.878766
Minimum0
Maximum377442
Zeros304860
Zeros (%)38.1%
Memory size6.1 MiB
2022-05-28T11:28:10.878150image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1197
Q310400.25
95-th percentile33414
Maximum377442
Range377442
Interquartile range (IQR)10400.25

Descriptive statistics

Standard deviation12708.08681
Coefficient of variation (CV)1.654294112
Kurtosis18.21038822
Mean7681.878766
Median Absolute Deviation (MAD)1197
Skewness2.733804806
Sum6140740248
Variance161495470.4
MonotocityNot monotonic
2022-05-28T11:28:11.095262image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
030486038.1%
 
2502105641.3%
 
262735180.4%
 
950032760.4%
 
275228640.4%
 
2531224850.3%
 
1335024050.3%
 
300223820.3%
 
2467122710.3%
 
200219380.2%
 
1585217950.2%
 
2371016030.2%
 
1602014660.2%
 
287714490.2%
 
760014280.2%
 
312814220.2%
 
2252714180.2%
 
1975813660.2%
 
2109313440.2%
 
2055913320.2%
 
1902213280.2%
 
1200213200.2%
 
330312870.2%
 
2563212030.2%
 
2274811960.1%
 
Other values (33020)44186055.3%
 
ValueCountFrequency (%) 
030486038.1%
 
51< 0.1%
 
62< 0.1%
 
92< 0.1%
 
152< 0.1%
 
162< 0.1%
 
186< 0.1%
 
416< 0.1%
 
422< 0.1%
 
573< 0.1%
 
ValueCountFrequency (%) 
3774421< 0.1%
 
3668302< 0.1%
 
3549261< 0.1%
 
3278141< 0.1%
 
2853851< 0.1%
 
2850011< 0.1%
 
2845222< 0.1%
 
2785152< 0.1%
 
2540541< 0.1%
 
2448862< 0.1%
 

ACT
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size46.5 MiB

EFF
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size46.5 MiB

HEAT_AR
Real number (ℝ≥0)

Distinct5907
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1821.918355
Minimum0
Maximum28363
Zeros1420
Zeros (%)0.2%
Memory size6.1 MiB
2022-05-28T11:28:11.284305image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile884
Q11272
median1647
Q32187
95-th percentile3330
Maximum28363
Range28363
Interquartile range (IQR)915

Descriptive statistics

Standard deviation809.1088727
Coefficient of variation (CV)0.4440972178
Kurtosis13.60003521
Mean1821.918355
Median Absolute Deviation (MAD)427
Skewness2.019152494
Sum1456405095
Variance654657.1679
MonotocityNot monotonic
2022-05-28T11:28:11.453429image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
120034100.4%
 
96034000.4%
 
115225320.3%
 
91221680.3%
 
151621290.3%
 
124821260.3%
 
129620590.3%
 
118420060.3%
 
126019640.2%
 
80019450.2%
 
144018390.2%
 
127218040.2%
 
108017500.2%
 
134417310.2%
 
98417300.2%
 
140417300.2%
 
124416340.2%
 
117616330.2%
 
116415680.2%
 
140015000.2%
 
110414900.2%
 
128014860.2%
 
140814720.2%
 
132014610.2%
 
86414500.2%
 
Other values (5882)75136394.0%
 
ValueCountFrequency (%) 
014200.2%
 
1401< 0.1%
 
1602< 0.1%
 
1922< 0.1%
 
2001< 0.1%
 
2321< 0.1%
 
2401< 0.1%
 
2721< 0.1%
 
2802< 0.1%
 
2882< 0.1%
 
ValueCountFrequency (%) 
283631< 0.1%
 
217963< 0.1%
 
189123< 0.1%
 
187031< 0.1%
 
185684< 0.1%
 
183993< 0.1%
 
177403< 0.1%
 
174001< 0.1%
 
159992< 0.1%
 
155471< 0.1%
 

ASD_VAL
Real number (ℝ≥0)

HIGH CORRELATION

Distinct219131
Distinct (%)27.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean205381.9479
Minimum2725
Maximum16539559
Zeros0
Zeros (%)0.0%
Memory size6.1 MiB
2022-05-28T11:28:11.830394image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2725
5-th percentile53820.85
Q1111233
median170111
Q3246150
95-th percentile457309
Maximum16539559
Range16536834
Interquartile range (IQR)134917

Descriptive statistics

Standard deviation181979.4556
Coefficient of variation (CV)0.8860538007
Kurtosis393.7890761
Mean205381.9479
Median Absolute Deviation (MAD)65518
Skewness10.07896159
Sum1.641782216e+11
Variance3.311652225e+10
MonotocityNot monotonic
2022-05-28T11:28:12.089654image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
92485244< 0.1%
 
74374210< 0.1%
 
56227194< 0.1%
 
117809192< 0.1%
 
30226187< 0.1%
 
71960179< 0.1%
 
80766164< 0.1%
 
100161154< 0.1%
 
157653142< 0.1%
 
59940139< 0.1%
 
146415136< 0.1%
 
119904134< 0.1%
 
38038127< 0.1%
 
66447127< 0.1%
 
111405125< 0.1%
 
95882124< 0.1%
 
66309123< 0.1%
 
61606120< 0.1%
 
45562119< 0.1%
 
92706118< 0.1%
 
45113118< 0.1%
 
65626107< 0.1%
 
30000107< 0.1%
 
47602105< 0.1%
 
126537104< 0.1%
 
Other values (219106)79578199.5%
 
ValueCountFrequency (%) 
27255< 0.1%
 
33533< 0.1%
 
35133< 0.1%
 
35417< 0.1%
 
365016< 0.1%
 
37523< 0.1%
 
385022< 0.1%
 
38931< 0.1%
 
39372< 0.1%
 
400817< 0.1%
 
ValueCountFrequency (%) 
165395593< 0.1%
 
105903841< 0.1%
 
87427943< 0.1%
 
81439044< 0.1%
 
73608962< 0.1%
 
65270161< 0.1%
 
63094414< 0.1%
 
60536262< 0.1%
 
59424691< 0.1%
 
58635472< 0.1%
 

TAX_VAL
Real number (ℝ≥0)

HIGH CORRELATION

Distinct213214
Distinct (%)26.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean174795.8381
Minimum31
Maximum16539559
Zeros0
Zeros (%)0.0%
Memory size6.1 MiB
2022-05-28T11:28:12.358899image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum31
5-th percentile25000
Q177300.75
median138297
Q3218434
95-th percentile427827.05
Maximum16539559
Range16539528
Interquartile range (IQR)141133.25

Descriptive statistics

Standard deviation182190.3379
Coefficient of variation (CV)1.042303638
Kurtosis393.6212172
Mean174795.8381
Median Absolute Deviation (MAD)68000
Skewness10.04549463
Sum1.397282971e+11
Variance3.319331923e+10
MonotocityNot monotonic
2022-05-28T11:28:12.558277image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
25000293653.7%
 
2450023980.3%
 
200004160.1%
 
92485244< 0.1%
 
74374199< 0.1%
 
117809194< 0.1%
 
56227191< 0.1%
 
30226187< 0.1%
 
71960171< 0.1%
 
100161154< 0.1%
 
80766153< 0.1%
 
59940147< 0.1%
 
157653142< 0.1%
 
66309130< 0.1%
 
66447130< 0.1%
 
111405129< 0.1%
 
61606126< 0.1%
 
38038125< 0.1%
 
95882125< 0.1%
 
119904124< 0.1%
 
146415124< 0.1%
 
45562121< 0.1%
 
45113119< 0.1%
 
92706109< 0.1%
 
47602108< 0.1%
 
Other values (213189)76394995.6%
 
ValueCountFrequency (%) 
311< 0.1%
 
324< 0.1%
 
341< 0.1%
 
371< 0.1%
 
391< 0.1%
 
441< 0.1%
 
551< 0.1%
 
593< 0.1%
 
661< 0.1%
 
771< 0.1%
 
ValueCountFrequency (%) 
165395593< 0.1%
 
105403841< 0.1%
 
87427943< 0.1%
 
80939044< 0.1%
 
73108962< 0.1%
 
64770161< 0.1%
 
62594414< 0.1%
 
60036262< 0.1%
 
58924691< 0.1%
 
58635472< 0.1%
 

SD1
Categorical

HIGH CARDINALITY

Distinct169
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.1 MiB
000
597204 
702
 
15704
006
 
12430
154
 
10313
037
 
9645
Other values (164)
154084 
ValueCountFrequency (%) 
00059720474.7%
 
702157042.0%
 
006124301.6%
 
154103131.3%
 
03796451.2%
 
00572010.9%
 
04762700.8%
 
04352050.7%
 
01250600.6%
 
01145900.6%
 
04136040.5%
 
03430460.4%
 
06330240.4%
 
05329100.4%
 
YGR27640.3%
 
04427090.3%
 
02126150.3%
 
09726100.3%
 
06125780.3%
 
05723770.3%
 
07723220.3%
 
06222080.3%
 
00221350.3%
 
00321110.3%
 
00820650.3%
 
Other values (144)8668010.8%
 
2022-05-28T11:28:12.771264image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-05-28T11:28:13.031956image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3

Overview of Unicode Properties

Unique unicode characters13
Unique unicode categories2 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
0199581783.2%
 
1876543.7%
 
7551062.3%
 
4504622.1%
 
2411101.7%
 
5396531.7%
 
3393711.6%
 
6388011.6%
 
9251531.0%
 
8167210.7%
 
Y27640.1%
 
G27640.1%
 
R27640.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number238984899.7%
 
Uppercase Letter82920.3%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
0199581783.5%
 
1876543.7%
 
7551062.3%
 
4504622.1%
 
2411101.7%
 
5396531.7%
 
3393711.6%
 
6388011.6%
 
9251531.1%
 
8167210.7%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
Y276433.3%
 
G276433.3%
 
R276433.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Common238984899.7%
 
Latin82920.3%
 

Most frequent Common characters

ValueCountFrequency (%) 
0199581783.5%
 
1876543.7%
 
7551062.3%
 
4504622.1%
 
2411101.7%
 
5396531.7%
 
3393711.6%
 
6388011.6%
 
9251531.1%
 
8167210.7%
 

Most frequent Latin characters

ValueCountFrequency (%) 
Y276433.3%
 
G276433.3%
 
R276433.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII2398140100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
0199581783.2%
 
1876543.7%
 
7551062.3%
 
4504622.1%
 
2411101.7%
 
5396531.7%
 
3393711.6%
 
6388011.6%
 
9251531.0%
 
8167210.7%
 
Y27640.1%
 
G27640.1%
 
R27640.1%
 

SD2
Categorical

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.1 MiB
000
794632 
201
 
3577
YGR
 
778
928
 
205
929
 
150
Other values (3)
 
38
ValueCountFrequency (%) 
00079463299.4%
 
20135770.4%
 
YGR7780.1%
 
928205< 0.1%
 
929150< 0.1%
 
70233< 0.1%
 
0073< 0.1%
 
1402< 0.1%
 
2022-05-28T11:28:13.146313image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-05-28T11:28:13.271380image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:28:13.465682image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3

Overview of Unicode Properties

Unique unicode characters10
Unique unicode categories2 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
0238751499.6%
 
239650.2%
 
135790.1%
 
Y778< 0.1%
 
G778< 0.1%
 
R778< 0.1%
 
9505< 0.1%
 
8205< 0.1%
 
736< 0.1%
 
42< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number239580699.9%
 
Uppercase Letter23340.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
0238751499.7%
 
239650.2%
 
135790.1%
 
9505< 0.1%
 
8205< 0.1%
 
736< 0.1%
 
42< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
Y77833.3%
 
G77833.3%
 
R77833.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Common239580699.9%
 
Latin23340.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
0238751499.7%
 
239650.2%
 
135790.1%
 
9505< 0.1%
 
8205< 0.1%
 
736< 0.1%
 
42< 0.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
Y77833.3%
 
G77833.3%
 
R77833.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII2398140100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
0238751499.6%
 
239650.2%
 
135790.1%
 
Y778< 0.1%
 
G778< 0.1%
 
R778< 0.1%
 
9505< 0.1%
 
8205< 0.1%
 
736< 0.1%
 
42< 0.1%
 

TIF
Categorical

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.1 MiB
0
740382 
E
 
29722
9
 
13728
1
 
5350
C
 
2105
Other values (10)
 
8093
ValueCountFrequency (%) 
074038292.6%
 
E297223.7%
 
9137281.7%
 
153500.7%
 
C21050.3%
 
618630.2%
 
D14610.2%
 
213760.2%
 
311020.1%
 
49640.1%
 
56710.1%
 
8270< 0.1%
 
A191< 0.1%
 
N185< 0.1%
 
B10< 0.1%
 
2022-05-28T11:28:13.648821image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-05-28T11:28:13.793146image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters15
Unique unicode categories2 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
074038292.6%
 
E297223.7%
 
9137281.7%
 
153500.7%
 
C21050.3%
 
618630.2%
 
D14610.2%
 
213760.2%
 
311020.1%
 
49640.1%
 
56710.1%
 
8270< 0.1%
 
A191< 0.1%
 
N185< 0.1%
 
B10< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number76570695.8%
 
Uppercase Letter336744.2%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
074038296.7%
 
9137281.8%
 
153500.7%
 
618630.2%
 
213760.2%
 
311020.1%
 
49640.1%
 
56710.1%
 
8270< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
E2972288.3%
 
C21056.3%
 
D14614.3%
 
A1910.6%
 
N1850.5%
 
B10< 0.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Common76570695.8%
 
Latin336744.2%
 

Most frequent Common characters

ValueCountFrequency (%) 
074038296.7%
 
9137281.8%
 
153500.7%
 
618630.2%
 
213760.2%
 
311020.1%
 
49640.1%
 
56710.1%
 
8270< 0.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
E2972288.3%
 
C21056.3%
 
D14614.3%
 
A1910.6%
 
N1850.5%
 
B10< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII799380100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
074038292.6%
 
E297223.7%
 
9137281.7%
 
153500.7%
 
C21050.3%
 
618630.2%
 
D14610.2%
 
213760.2%
 
311020.1%
 
49640.1%
 
56710.1%
 
8270< 0.1%
 
A191< 0.1%
 
N185< 0.1%
 
B10< 0.1%
 

BASE
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size39.8 MiB

ACREAGE
Real number (ℝ≥0)

SKEWED

Distinct209841
Distinct (%)26.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2482290021
Minimum2.03714e-05
Maximum102.646
Zeros0
Zeros (%)0.0%
Memory size6.1 MiB
2022-05-28T11:28:14.109813image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2.03714e-05
5-th percentile0.008953248
Q10.11168
median0.165652
Q30.238748
95-th percentile0.661385
Maximum102.646
Range102.6459796
Interquartile range (IQR)0.127068

Descriptive statistics

Standard deviation0.5797951532
Coefficient of variation (CV)2.335726882
Kurtosis6486.471154
Mean0.2482290021
Median Absolute Deviation (MAD)0.062701
Skewness46.57661552
Sum198429.2997
Variance0.3361624197
MonotocityNot monotonic
2022-05-28T11:28:14.297528image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
5.16527e-05118731.5%
 
9.1827e-05110751.4%
 
5.1664e-0534570.4%
 
5.16414e-0528330.4%
 
0.12626328240.4%
 
0.13774118690.2%
 
0.12626216280.2%
 
0.1010114650.2%
 
0.12626410880.1%
 
0.1147848650.1%
 
0.1320027860.1%
 
0.1515157100.1%
 
0.01019297100.1%
 
0.137746590.1%
 
0.1388896390.1%
 
0.1010116220.1%
 
0.1262655980.1%
 
0.1377425820.1%
 
0.01019285790.1%
 
0.1652895770.1%
 
0.1262665410.1%
 
0.1101935220.1%
 
0.1262615190.1%
 
0.1010094930.1%
 
0.1147834690.1%
 
Other values (209816)75139794.0%
 
ValueCountFrequency (%) 
2.03714e-055< 0.1%
 
3.80899e-053< 0.1%
 
4.20554e-051< 0.1%
 
4.4168e-052< 0.1%
 
4.72805e-051< 0.1%
 
4.78615e-052< 0.1%
 
4.84166e-052< 0.1%
 
4.89977e-052< 0.1%
 
5.16177e-052< 0.1%
 
5.16219e-051< 0.1%
 
ValueCountFrequency (%) 
102.6465< 0.1%
 
40.8991< 0.1%
 
40.30082< 0.1%
 
39.6942< 0.1%
 
38.60561< 0.1%
 
37.2812< 0.1%
 
34.33181< 0.1%
 
29.52321< 0.1%
 
26.82861< 0.1%
 
25.49251< 0.1%
 

NBHC
Categorical

HIGH CARDINALITY

Distinct313
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.1 MiB
228003.0
 
20316
224005.0
 
14986
228004.0
 
11673
212003.0
 
10738
225001.0
 
10707
Other values (308)
730960 
ValueCountFrequency (%) 
228003.0203162.5%
 
224005.0149861.9%
 
228004.0116731.5%
 
212003.0107381.3%
 
225001.0107071.3%
 
212004.092191.2%
 
227001.085421.1%
 
226002.082411.0%
 
220003.081271.0%
 
212006.078871.0%
 
202001.072980.9%
 
209012.070140.9%
 
223008.066070.8%
 
222006.064490.8%
 
212008.062300.8%
 
222002.060300.8%
 
223009.059860.7%
 
223001.059620.7%
 
226001.058350.7%
 
220004.058060.7%
 
214006.057280.7%
 
216002.054690.7%
 
227003.054290.7%
 
226003.054070.7%
 
210010.053670.7%
 
Other values (288)59832774.8%
 
2022-05-28T11:28:15.150836image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-05-28T11:28:15.332522image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length8
Median length8
Mean length8
Min length8

Overview of Unicode Properties

Unique unicode characters11
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
0258633040.4%
 
2133225820.8%
 
.79938012.5%
 
15725699.0%
 
32367313.7%
 
62079523.3%
 
51642782.6%
 
41614172.5%
 
71249612.0%
 
81213441.9%
 
9878201.4%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number559566087.5%
 
Other Punctuation79938012.5%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
0258633046.2%
 
2133225823.8%
 
157256910.2%
 
32367314.2%
 
62079523.7%
 
51642782.9%
 
41614172.9%
 
71249612.2%
 
81213442.2%
 
9878201.6%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.799380100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common6395040100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
0258633040.4%
 
2133225820.8%
 
.79938012.5%
 
15725699.0%
 
32367313.7%
 
62079523.3%
 
51642782.6%
 
41614172.5%
 
71249612.0%
 
81213441.9%
 
9878201.4%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII6395040100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
0258633040.4%
 
2133225820.8%
 
.79938012.5%
 
15725699.0%
 
32367313.7%
 
62079523.3%
 
51642782.6%
 
41614172.5%
 
71249612.0%
 
81213441.9%
 
9878201.4%
 

MUNICIPALITY_CD
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.0 KiB
U
552971 
A
211992 
P
 
18584
T
 
15833
ValueCountFrequency (%) 
U55297169.2%
 
A21199226.5%
 
P185842.3%
 
T158332.0%
 
2022-05-28T11:28:15.482452image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-05-28T11:28:15.565586image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:28:15.698186image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters4
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
U55297169.2%
 
A21199226.5%
 
P185842.3%
 
T158332.0%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter799380100.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
U55297169.2%
 
A21199226.5%
 
P185842.3%
 
T158332.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin799380100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
U55297169.2%
 
A21199226.5%
 
P185842.3%
 
T158332.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII799380100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
U55297169.2%
 
A21199226.5%
 
P185842.3%
 
T158332.0%
 

SECTION_CD
Categorical

Distinct36
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size782.1 KiB
10
 
28819
06
 
28297
07
 
27984
05
 
27818
12
 
27350
Other values (31)
659112 
ValueCountFrequency (%) 
10288193.6%
 
06282973.5%
 
07279843.5%
 
05278183.5%
 
12273503.4%
 
04270883.4%
 
17268703.4%
 
33268473.4%
 
11246773.1%
 
32244813.1%
 
08237753.0%
 
20234192.9%
 
14229452.9%
 
21227872.9%
 
28227662.8%
 
23223162.8%
 
36216532.7%
 
22216322.7%
 
18216272.7%
 
13215472.7%
 
26213872.7%
 
27213422.7%
 
15212662.7%
 
25210962.6%
 
01208112.6%
 
Other values (11)19878024.9%
 
2022-05-28T11:28:15.873411image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-05-28T11:28:16.012263image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length2
Min length2

Overview of Unicode Properties

Unique unicode characters10
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
131296719.6%
 
230740219.2%
 
028457217.8%
 
323126414.5%
 
6916175.7%
 
4882865.5%
 
5872255.5%
 
7761964.8%
 
8681684.3%
 
9510633.2%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number1598760100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
131296719.6%
 
230740219.2%
 
028457217.8%
 
323126414.5%
 
6916175.7%
 
4882865.5%
 
5872255.5%
 
7761964.8%
 
8681684.3%
 
9510633.2%
 

Most occurring scripts

ValueCountFrequency (%) 
Common1598760100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
131296719.6%
 
230740219.2%
 
028457217.8%
 
323126414.5%
 
6916175.7%
 
4882865.5%
 
5872255.5%
 
7761964.8%
 
8681684.3%
 
9510633.2%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1598760100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
131296719.6%
 
230740219.2%
 
028457217.8%
 
323126414.5%
 
6916175.7%
 
4882865.5%
 
5872255.5%
 
7761964.8%
 
8681684.3%
 
9510633.2%
 

TOWNSHIP_CD
Categorical

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.0 KiB
28
240792 
29
203954 
30
135415 
27
108337 
31
61029 
ValueCountFrequency (%) 
2824079230.1%
 
2920395425.5%
 
3013541516.9%
 
2710833713.6%
 
31610297.6%
 
32498536.2%
 
2022-05-28T11:28:16.135195image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-05-28T11:28:16.250545image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:28:16.347566image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length2
Min length2

Overview of Unicode Properties

Unique unicode characters7
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
260293637.7%
 
324629715.4%
 
824079215.1%
 
920395412.8%
 
01354158.5%
 
71083376.8%
 
1610293.8%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number1598760100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
260293637.7%
 
324629715.4%
 
824079215.1%
 
920395412.8%
 
01354158.5%
 
71083376.8%
 
1610293.8%
 

Most occurring scripts

ValueCountFrequency (%) 
Common1598760100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
260293637.7%
 
324629715.4%
 
824079215.1%
 
920395412.8%
 
01354158.5%
 
71083376.8%
 
1610293.8%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1598760100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
260293637.7%
 
324629715.4%
 
824079215.1%
 
920395412.8%
 
01354158.5%
 
71083376.8%
 
1610293.8%
 

RANGE_CD
Categorical

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.0 KiB
18
237197 
20
199997 
19
189845 
17
94080 
21
60380 
ValueCountFrequency (%) 
1823719729.7%
 
2019999725.0%
 
1918984523.7%
 
179408011.8%
 
21603807.6%
 
22178812.2%
 
2022-05-28T11:28:16.462676image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-05-28T11:28:16.537965image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:28:16.634038image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length2
Min length2

Overview of Unicode Properties

Unique unicode characters6
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
158150236.4%
 
229613918.5%
 
823719714.8%
 
019999712.5%
 
918984511.9%
 
7940805.9%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number1598760100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
158150236.4%
 
229613918.5%
 
823719714.8%
 
019999712.5%
 
918984511.9%
 
7940805.9%
 

Most occurring scripts

ValueCountFrequency (%) 
Common1598760100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
158150236.4%
 
229613918.5%
 
823719714.8%
 
019999712.5%
 
918984511.9%
 
7940805.9%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1598760100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
158150236.4%
 
229613918.5%
 
823719714.8%
 
019999712.5%
 
918984511.9%
 
7940805.9%
 

BLOCK_NUM
Categorical

HIGH CARDINALITY

Distinct885
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.6 MiB
000000
192054 
000001
94351 
000002
71442 
000003
53146 
000004
42096 
Other values (880)
346291 
ValueCountFrequency (%) 
00000019205424.0%
 
0000019435111.8%
 
000002714428.9%
 
000003531466.6%
 
000004420965.3%
 
000005343374.3%
 
000006239363.0%
 
000007187722.3%
 
000008162122.0%
 
A00000150041.9%
 
000009141901.8%
 
000010122531.5%
 
B00000118681.5%
 
00001199151.2%
 
C0000090561.1%
 
00001284621.1%
 
00001476411.0%
 
00001376071.0%
 
00001566830.8%
 
00001664430.8%
 
D0000060080.8%
 
00001758770.7%
 
00001844750.6%
 
E0000043410.5%
 
00001943200.5%
 
Other values (860)11889114.9%
 
2022-05-28T11:28:16.779241image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique22 ?
Unique (%)< 0.1%
2022-05-28T11:28:16.911394image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length6
Mean length6
Min length6

Overview of Unicode Properties

Unique unicode characters36
Unique unicode categories2 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
0402632983.9%
 
11982384.1%
 
21273112.7%
 
3927341.9%
 
4701611.5%
 
5586141.2%
 
6455450.9%
 
7379500.8%
 
8319300.7%
 
9293460.6%
 
A176340.4%
 
B153390.3%
 
C109120.2%
 
D72910.2%
 
E54080.1%
 
F35440.1%
 
G30330.1%
 
H2390< 0.1%
 
I1953< 0.1%
 
K1490< 0.1%
 
J1378< 0.1%
 
L1106< 0.1%
 
T930< 0.1%
 
N797< 0.1%
 
M775< 0.1%
 
Other values (11)41420.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number471815898.4%
 
Uppercase Letter781221.6%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
0402632985.3%
 
11982384.2%
 
21273112.7%
 
3927342.0%
 
4701611.5%
 
5586141.2%
 
6455451.0%
 
7379500.8%
 
8319300.7%
 
9293460.6%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A1763422.6%
 
B1533919.6%
 
C1091214.0%
 
D72919.3%
 
E54086.9%
 
F35444.5%
 
G30333.9%
 
H23903.1%
 
I19532.5%
 
K14901.9%
 
J13781.8%
 
L11061.4%
 
T9301.2%
 
N7971.0%
 
M7751.0%
 
P7410.9%
 
S6750.9%
 
O5710.7%
 
Q5670.7%
 
R5150.7%
 
U3010.4%
 
W2620.3%
 
V2610.3%
 
X1520.2%
 
Y500.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Common471815898.4%
 
Latin781221.6%
 

Most frequent Common characters

ValueCountFrequency (%) 
0402632985.3%
 
11982384.2%
 
21273112.7%
 
3927342.0%
 
4701611.5%
 
5586141.2%
 
6455451.0%
 
7379500.8%
 
8319300.7%
 
9293460.6%
 

Most frequent Latin characters

ValueCountFrequency (%) 
A1763422.6%
 
B1533919.6%
 
C1091214.0%
 
D72919.3%
 
E54086.9%
 
F35444.5%
 
G30333.9%
 
H23903.1%
 
I19532.5%
 
K14901.9%
 
J13781.8%
 
L11061.4%
 
T9301.2%
 
N7971.0%
 
M7751.0%
 
P7410.9%
 
S6750.9%
 
O5710.7%
 
Q5670.7%
 
R5150.7%
 
U3010.4%
 
W2620.3%
 
V2610.3%
 
X1520.2%
 
Y500.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII4796280100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
0402632983.9%
 
11982384.1%
 
21273112.7%
 
3927341.9%
 
4701611.5%
 
5586141.2%
 
6455450.9%
 
7379500.8%
 
8319300.7%
 
9293460.6%
 
A176340.4%
 
B153390.3%
 
C109120.2%
 
D72910.2%
 
E54080.1%
 
F35440.1%
 
G30330.1%
 
H2390< 0.1%
 
I1953< 0.1%
 
K1490< 0.1%
 
J1378< 0.1%
 
L1106< 0.1%
 
T930< 0.1%
 
N797< 0.1%
 
M775< 0.1%
 
Other values (11)41420.1%
 

LOT_NUM
Categorical

HIGH CARDINALITY

Distinct19038
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
00001.0
 
33892
00003.0
 
32586
00002.0
 
31662
00004.0
 
31380
00005.0
 
29346
Other values (19033)
640514 
ValueCountFrequency (%) 
00001.0338924.2%
 
00003.0325864.1%
 
00002.0316624.0%
 
00004.0313803.9%
 
00005.0293463.7%
 
00006.0277263.5%
 
00007.0248643.1%
 
00008.0238683.0%
 
00009.0213802.7%
 
00010.0202612.5%
 
00011.0197772.5%
 
00012.0179432.2%
 
00013.0176932.2%
 
00014.0164682.1%
 
00015.0158382.0%
 
00016.0149281.9%
 
00017.0143531.8%
 
00018.0131891.6%
 
00019.0126191.6%
 
00020.0117781.5%
 
00021.0110661.4%
 
00022.0106131.3%
 
00023.098551.2%
 
00024.089761.1%
 
00025.082321.0%
 
Other values (19013)31908739.9%
 
2022-05-28T11:28:17.071162image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique3602 ?
Unique (%)0.5%
2022-05-28T11:28:17.209360image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length7
Median length7
Mean length7
Min length7

Overview of Unicode Properties

Unique unicode characters35
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
0335345759.9%
 
.79938014.3%
 
13627986.5%
 
22328124.2%
 
31743423.1%
 
41410142.5%
 
51220832.2%
 
61100502.0%
 
71008461.8%
 
8945951.7%
 
9852481.5%
 
A43640.1%
 
B35030.1%
 
C2301< 0.1%
 
D1653< 0.1%
 
E1463< 0.1%
 
F853< 0.1%
 
G644< 0.1%
 
H553< 0.1%
 
W535< 0.1%
 
J402< 0.1%
 
L358< 0.1%
 
I329< 0.1%
 
K295< 0.1%
 
N275< 0.1%
 
Other values (10)1507< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number477724585.4%
 
Other Punctuation79938014.3%
 
Uppercase Letter190350.3%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
0335345770.2%
 
13627987.6%
 
22328124.9%
 
31743423.6%
 
41410143.0%
 
51220832.6%
 
61100502.3%
 
71008462.1%
 
8945952.0%
 
9852481.8%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.799380100.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A436422.9%
 
B350318.4%
 
C230112.1%
 
D16538.7%
 
E14637.7%
 
F8534.5%
 
G6443.4%
 
H5532.9%
 
W5352.8%
 
J4022.1%
 
L3581.9%
 
I3291.7%
 
K2951.5%
 
N2751.4%
 
M2751.4%
 
S2611.4%
 
T2091.1%
 
P2091.1%
 
V1700.9%
 
O1250.7%
 
Q930.5%
 
R650.3%
 
X640.3%
 
U360.2%
 

Most occurring scripts

ValueCountFrequency (%) 
Common557662599.7%
 
Latin190350.3%
 

Most frequent Common characters

ValueCountFrequency (%) 
0335345760.1%
 
.79938014.3%
 
13627986.5%
 
22328124.2%
 
31743423.1%
 
41410142.5%
 
51220832.2%
 
61100502.0%
 
71008461.8%
 
8945951.7%
 
9852481.5%
 

Most frequent Latin characters

ValueCountFrequency (%) 
A436422.9%
 
B350318.4%
 
C230112.1%
 
D16538.7%
 
E14637.7%
 
F8534.5%
 
G6443.4%
 
H5532.9%
 
W5352.8%
 
J4022.1%
 
L3581.9%
 
I3291.7%
 
K2951.5%
 
N2751.4%
 
M2751.4%
 
S2611.4%
 
T2091.1%
 
P2091.1%
 
V1700.9%
 
O1250.7%
 
Q930.5%
 
R650.3%
 
X640.3%
 
U360.2%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII5595660100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
0335345759.9%
 
.79938014.3%
 
13627986.5%
 
22328124.2%
 
31743423.1%
 
41410142.5%
 
51220832.2%
 
61100502.0%
 
71008461.8%
 
8945951.7%
 
9852481.5%
 
A43640.1%
 
B35030.1%
 
C2301< 0.1%
 
D1653< 0.1%
 
E1463< 0.1%
 
F853< 0.1%
 
G644< 0.1%
 
H553< 0.1%
 
W535< 0.1%
 
J402< 0.1%
 
L358< 0.1%
 
I329< 0.1%
 
K295< 0.1%
 
N275< 0.1%
 
Other values (10)1507< 0.1%
 

MARKET_AREA_CD
Categorical

HIGH CORRELATION

Distinct29
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.1 MiB
26
 
54698
12
 
53673
23
 
49252
20
 
44512
16
 
44059
Other values (24)
553186 
ValueCountFrequency (%) 
26546986.8%
 
12536736.7%
 
23492526.2%
 
20445125.6%
 
16440595.5%
 
03383074.8%
 
28359554.5%
 
27344594.3%
 
15329994.1%
 
06280253.5%
 
24279143.5%
 
10278873.5%
 
09266933.3%
 
05264083.3%
 
21263963.3%
 
22250453.1%
 
14238723.0%
 
08225482.8%
 
02225122.8%
 
13222922.8%
 
17217642.7%
 
25213222.7%
 
01210442.6%
 
04152901.9%
 
07141821.8%
 
Other values (4)382724.8%
 
2022-05-28T11:28:17.358371image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-05-28T11:28:17.512040image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length2
Min length2

Overview of Unicode Properties

Unique unicode characters10
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
242762526.7%
 
131691919.8%
 
028740818.0%
 
61267827.9%
 
31098516.9%
 
5807295.0%
 
8714724.5%
 
7704054.4%
 
4670764.2%
 
9404932.5%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number1598760100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
242762526.7%
 
131691919.8%
 
028740818.0%
 
61267827.9%
 
31098516.9%
 
5807295.0%
 
8714724.5%
 
7704054.4%
 
4670764.2%
 
9404932.5%
 

Most occurring scripts

ValueCountFrequency (%) 
Common1598760100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
242762526.7%
 
131691919.8%
 
028740818.0%
 
61267827.9%
 
31098516.9%
 
5807295.0%
 
8714724.5%
 
7704054.4%
 
4670764.2%
 
9404932.5%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII1598760100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
242762526.7%
 
131691919.8%
 
028740818.0%
 
61267827.9%
 
31098516.9%
 
5807295.0%
 
8714724.5%
 
7704054.4%
 
4670764.2%
 
9404932.5%
 

REGION
Categorical

HIGH CORRELATION

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.1 MiB
Northern
198659 
Eastern
153186 
Tampa
106453 
Northeast
90835 
South_Tampa
81863 
Other values (3)
168384 
ValueCountFrequency (%) 
Northern19865924.9%
 
Eastern15318619.2%
 
Tampa10645313.3%
 
Northeast9083511.4%
 
South_Tampa8186310.2%
 
Northwest660838.3%
 
East_Bay595047.4%
 
Southern427975.4%
 
2022-05-28T11:28:17.692539image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2022-05-28T11:28:17.841067image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:28:18.068408image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length11
Median length8
Mean length7.912384598
Min length5

Overview of Unicode Properties

Unique unicode characters19
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
t84984513.4%
 
r75021911.9%
 
a73966111.7%
 
e5515608.7%
 
o4802377.6%
 
h4802377.6%
 
n3946426.2%
 
s3696085.8%
 
N3555775.6%
 
E2126903.4%
 
T1883163.0%
 
m1883163.0%
 
p1883163.0%
 
_1413672.2%
 
S1246602.0%
 
u1246602.0%
 
w660831.0%
 
B595040.9%
 
y595040.9%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter524288882.9%
 
Uppercase Letter94074714.9%
 
Connector Punctuation1413672.2%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N35557737.8%
 
E21269022.6%
 
T18831620.0%
 
S12466013.3%
 
B595046.3%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
t84984516.2%
 
r75021914.3%
 
a73966114.1%
 
e55156010.5%
 
o4802379.2%
 
h4802379.2%
 
n3946427.5%
 
s3696087.0%
 
m1883163.6%
 
p1883163.6%
 
u1246602.4%
 
w660831.3%
 
y595041.1%
 

Most frequent Connector Punctuation characters

ValueCountFrequency (%) 
_141367100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin618363597.8%
 
Common1413672.2%
 

Most frequent Latin characters

ValueCountFrequency (%) 
t84984513.7%
 
r75021912.1%
 
a73966112.0%
 
e5515608.9%
 
o4802377.8%
 
h4802377.8%
 
n3946426.4%
 
s3696086.0%
 
N3555775.8%
 
E2126903.4%
 
T1883163.0%
 
m1883163.0%
 
p1883163.0%
 
S1246602.0%
 
u1246602.0%
 
w660831.1%
 
B595041.0%
 
y595041.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
_141367100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII6325002100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
t84984513.4%
 
r75021911.9%
 
a73966111.7%
 
e5515608.7%
 
o4802377.6%
 
h4802377.6%
 
n3946426.2%
 
s3696085.8%
 
N3555775.6%
 
E2126903.4%
 
T1883163.0%
 
m1883163.0%
 
p1883163.0%
 
_1413672.2%
 
S1246602.0%
 
u1246602.0%
 
w660831.0%
 
B595040.9%
 
y595040.9%
 

AGE
Real number (ℝ)

ZEROS

Distinct176
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.44565038
Minimum-41
Maximum143
Zeros165687
Zeros (%)20.7%
Memory size6.1 MiB
2022-05-28T11:28:18.319792image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-41
5-th percentile0
Q11
median12
Q329
95-th percentile61
Maximum143
Range184
Interquartile range (IQR)28

Descriptive statistics

Standard deviation20.85708273
Coefficient of variation (CV)1.13073176
Kurtosis1.673092321
Mean18.44565038
Median Absolute Deviation (MAD)12
Skewness1.281554958
Sum14745084
Variance435.0178998
MonotocityNot monotonic
2022-05-28T11:28:18.586854image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
016568720.7%
 
1286623.6%
 
4198932.5%
 
3191332.4%
 
5190092.4%
 
2181392.3%
 
6174142.2%
 
7170082.1%
 
9167092.1%
 
8166452.1%
 
10163042.0%
 
12162962.0%
 
11162032.0%
 
14160582.0%
 
13157692.0%
 
15157352.0%
 
16146251.8%
 
18132521.7%
 
17131811.6%
 
19129821.6%
 
20119991.5%
 
21109521.4%
 
22104481.3%
 
23100101.3%
 
2493891.2%
 
Other values (151)25787832.3%
 
ValueCountFrequency (%) 
-4111< 0.1%
 
-4035< 0.1%
 
-3954< 0.1%
 
-3866< 0.1%
 
-3779< 0.1%
 
-3694< 0.1%
 
-35106< 0.1%
 
-34132< 0.1%
 
-33138< 0.1%
 
-32171< 0.1%
 
ValueCountFrequency (%) 
1431< 0.1%
 
1331< 0.1%
 
1321< 0.1%
 
13113< 0.1%
 
13011< 0.1%
 
1294< 0.1%
 
12812< 0.1%
 
12711< 0.1%
 
1268< 0.1%
 
1259< 0.1%
 

Interactions

2022-05-28T11:24:59.894343image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:00.226644image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:00.680197image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:01.327190image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:01.804723image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:02.361242image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:02.866449image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:03.346564image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:03.877120image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:04.359094image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:04.959597image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:05.576634image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:06.110133image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:06.626156image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:07.213637image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:07.803045image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:08.359333image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:08.946229image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:09.500284image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:10.208349image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:10.875189image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:11.598862image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:12.273278image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:12.942993image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:13.492166image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:14.042985image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:14.582072image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:15.097562image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:15.531515image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:16.007867image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:16.436373image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:17.014525image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:17.511478image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:18.056922image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:18.585558image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:19.121918image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:19.625362image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:20.121637image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:20.481772image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:20.856225image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:21.244281image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:21.587991image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:21.983992image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:22.350759image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:22.697342image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:23.113095image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:23.434582image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:23.775406image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:24.084653image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:24.393196image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:24.700368image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:25.047381image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:25.410009image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:25.779745image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:26.125712image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:26.456525image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:26.894068image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:27.346609image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:27.875171image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:28.441327image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:28.962270image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:29.407400image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:29.851610image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:30.334276image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:30.755189image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:31.208042image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:31.652574image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:32.060280image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:32.482559image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:32.952842image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:33.364657image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:33.794810image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:34.233417image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:34.775996image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:35.388337image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:35.839868image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:36.332350image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:36.789541image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:37.336504image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:37.807361image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:38.241428image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:38.680260image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:39.238328image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:39.741146image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:40.307251image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:40.771224image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:41.269598image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:41.772375image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:42.272615image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:42.736349image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:43.252604image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:43.872278image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:44.501716image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:45.059191image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:45.617803image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:46.159283image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:46.671099image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:47.179082image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:47.637878image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:48.013009image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:48.522980image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:48.943938image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:49.478289image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:49.864046image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:50.282536image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:50.895405image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:51.549031image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:52.129638image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:52.645329image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:53.150100image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:53.760922image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:54.271321image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:54.764859image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:55.143747image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:55.723128image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:56.105814image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:56.617559image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:57.179206image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:57.644187image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:58.060276image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:58.452519image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:59.058605image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:25:59.748117image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:00.358696image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:00.980759image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:01.446385image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:01.925863image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:02.480170image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:03.102853image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:04.143960image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:04.561054image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:05.078018image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:05.517432image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:06.006694image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:06.365567image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:06.868500image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:07.413806image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:07.946011image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:08.430434image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:08.875260image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:09.334807image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:09.770446image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:10.256058image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:10.930744image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:11.446035image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:11.919564image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:12.573056image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:13.050817image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:13.506016image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:14.039019image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:14.408302image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:14.835469image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:15.238141image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:15.776174image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:16.328370image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:16.900965image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:17.517500image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:18.008974image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:18.459738image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:18.998995image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:19.591133image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:20.040068image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:20.616392image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:21.120383image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:21.590257image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:22.128514image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:22.636096image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:23.213571image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:23.695889image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:24.145397image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:24.634229image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:25.034652image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:25.587193image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:26.090621image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:26.553532image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:27.073876image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:27.578417image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:28.152805image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:28.609524image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:29.101288image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:29.505480image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:29.970624image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:30.374245image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:30.830393image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:31.260361image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:31.786036image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:32.295789image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:32.859394image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:33.327288image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:33.729464image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:34.120232image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:34.787210image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:35.105326image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:35.444593image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:35.858794image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:36.387513image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:36.912664image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:37.425692image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:37.934370image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:38.408010image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:38.818555image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:39.417242image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:40.113435image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:40.858574image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:41.614284image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:42.359929image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:43.152551image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:43.831702image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:44.570231image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:45.273324image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:46.019776image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:47.032049image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:48.017302image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:48.677709image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:49.366070image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:50.075889image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:50.885104image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:51.623056image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:52.335414image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:53.128234image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:53.715614image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:54.354228image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:55.032227image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:55.783122image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:56.469832image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:57.151844image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:57.932477image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:58.520749image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:59.137568image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:26:59.861811image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:27:00.529019image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:27:01.209991image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:27:01.884865image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:27:02.706156image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:27:03.481637image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:27:04.255820image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:27:04.963192image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:27:05.720236image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:27:06.358615image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:27:07.147056image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:27:07.809483image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:27:08.578140image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:27:09.413607image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:27:10.151063image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:27:10.929018image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:27:11.717520image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:27:12.652939image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:27:13.520098image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:27:14.271934image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:27:15.055325image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:27:15.789889image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:27:16.614679image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:27:17.484527image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:27:18.180521image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:27:18.646688image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:27:19.404336image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-05-28T11:28:18.835749image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-05-28T11:28:19.217516image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-05-28T11:28:19.595106image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-05-28T11:28:19.994425image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2022-05-28T11:28:20.506906image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2022-05-28T11:27:29.663453image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:27:36.823887image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:27:47.814261image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-05-28T11:27:52.883127image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Sample

First rows

df_indexFOLIODOR_CODES_DATEVIQUREA_CDS_AMTSUBS_TYPEORIG_SALES_DATESITE_ADDRSITE_CITYSITE_ZIPtBEDStBATHStSTORIEStUNITStBLDGSJUSTLANDBLDGEXFACTEFFHEAT_ARASD_VALTAX_VALSD1SD2TIFBASEACREAGENBHCMUNICIPALITY_CDSECTION_CDTOWNSHIP_CDRANGE_CDBLOCK_NUMLOT_NUMMARKET_AREA_CDREGIONAGE
08000008010001001987-08-01IQ0150000.0001WD1985-11-0119859 ANGEL LNODESSA335563.02.02.01.01.0565190.0174976.0384856.05358.0199620082617.0418262.0368262.0000000020165.058780211007.0U01271700000000001.111Northwest-9
111000009010001002021-10-27IQ01750000.0001WD1973-01-0119913 ANGEL LNODESSA335563.02.51.01.01.0453092.0272419.0169047.011626.0197619981572.0453092.0453092.0000000019734.438490211007.0U01271700000000002.111Northwest45
214000009010001001997-05-01IQ01169900.0001WD1973-01-0119913 ANGEL LNODESSA335563.02.51.01.01.0453092.0272419.0169047.011626.0197619981572.0453092.0453092.0000000019734.438490211007.0U01271700000000002.111Northwest21
320000010000001001988-06-01IQ0152500.0001WD1977-12-016934 W COUNTY LINE RDODESSA335563.02.01.01.01.0260068.076500.0133128.050440.0192619732143.0173560.0123560.0000000019940.992559211007.0U01271700000000003.011Northwest62
421000010000001001983-02-01IQ0130000.0001WD1977-12-016934 W COUNTY LINE RDODESSA335563.02.01.01.01.0260068.076500.0133128.050440.0192619732143.0173560.0123560.0000000019940.992559211007.0U01271700000000003.011Northwest57
523000010000201002012-06-19IQ02272500.0001WD1991-04-017010 W COUNTY LINE RDODESSA335564.03.01.01.01.0395192.075865.0287304.032023.0198720032971.0223410.0173410.0000000020131.309540211007.0U01271700000000004.211Northwest25
624000010000201002004-06-01IQ01207500.0001WD1991-04-017010 W COUNTY LINE RDODESSA335564.03.01.01.01.0395192.075865.0287304.032023.0198720032971.0223410.0173410.0000000020131.309540211007.0U01271700000000004.211Northwest17
725000010000201001999-02-01IQ01145000.0001WD1991-04-017010 W COUNTY LINE RDODESSA335564.03.01.01.01.0395192.075865.0287304.032023.0198720032971.0223410.0173410.0000000020131.309540211007.0U01271700000000004.211Northwest12
826000010000201001992-12-01IQ01109900.0001WD1991-04-017010 W COUNTY LINE RDODESSA335564.03.01.01.01.0395192.075865.0287304.032023.0198720032971.0223410.0173410.0000000020131.309540211007.0U01271700000000004.211Northwest5
927000010000201001991-04-01IQ0185000.0001WD1991-04-017010 W COUNTY LINE RDODESSA335564.03.01.01.01.0395192.075865.0287304.032023.0198720032971.0223410.0173410.0000000020131.309540211007.0U01271700000000004.211Northwest4

Last rows

df_indexFOLIODOR_CODES_DATEVIQUREA_CDS_AMTSUBS_TYPEORIG_SALES_DATESITE_ADDRSITE_CITYSITE_ZIPtBEDStBATHStSTORIEStUNITStBLDGSJUSTLANDBLDGEXFACTEFFHEAT_ARASD_VALTAX_VALSD1SD2TIFBASEACREAGENBHCMUNICIPALITY_CDSECTION_CDTOWNSHIP_CDRANGE_CDBLOCK_NUMLOT_NUMMARKET_AREA_CDREGIONAGE
7993702047188209433010001002017-11-14IQ02191500.05ELWD1984-05-01705 E LEE STPLANT CITY335634.03.01.01.01.0280552.040400.0239243.0909.0200620131446.0150701.0100701.0000000020180.183655221006.0P33282200000500003.021Northeast11
7993712047190209433010001002012-03-02IQ02121000.05ELWD1984-05-01705 E LEE STPLANT CITY335634.03.01.01.01.0280552.040400.0239243.0909.0200620131446.0150701.0100701.0000000020180.183655221006.0P33282200000500003.021Northeast6
7993722047201209435000001002000-01-10IQ0185000.05ELWD1971-01-01704 E DREW STPLANT CITY335633.02.01.01.01.0217427.075200.0139350.02877.0199020051195.0116985.0116985.0000000019710.367309221006.0P33282200000500006.021Northeast10
7993732047208209436000001002021-06-30IQ02169000.05ELWD1980-12-01708 E DREW STPLANT CITY335632.01.01.01.01.0132090.040400.090343.01347.019381995840.0132090.0132090.0000000019800.174472221006.0P33282200000500008.021Northeast83
7993742047211209436000001002012-12-10IQ2A35700.05ELWD1980-12-01708 E DREW STPLANT CITY335632.01.01.01.01.0132090.040400.090343.01347.019381995840.0132090.0132090.0000000019800.174472221006.0P33282200000500008.021Northeast74
7993752047213209436000001002006-02-28IQ02123900.05ELWD1980-12-01708 E DREW STPLANT CITY335632.01.01.01.01.0132090.040400.090343.01347.019381995840.0132090.0132090.0000000019800.174472221006.0P33282200000500008.021Northeast68
7993762047214209436000001002005-09-07IQ0175000.05ELWD1980-12-01708 E DREW STPLANT CITY335632.01.01.01.01.0132090.040400.090343.01347.019381995840.0132090.0132090.0000000019800.174472221006.0P33282200000500008.021Northeast67
7993772047220209436005001002006-10-25IQ01200000.05ELWD2005-08-23710 E DREW STPLANT CITY33563-66023.02.01.01.01.0196313.040400.0155546.0367.0200520131290.067435.025000.0000000020110.183654221006.0P33282200000500010.021Northeast1
7993782047223209436010001002015-08-21IQ2A134000.05ELWD2005-08-23712 E DREW STPLANT CITY335633.02.01.01.01.0204434.047685.0156004.0745.0200620161290.099696.049696.0000000020160.288579221006.0P33282200000500011.021Northeast9
7993792047226209436010001002006-04-28IQ01195000.05ELWD2005-08-23712 E DREW STPLANT CITY335633.02.01.01.01.0204434.047685.0156004.0745.0200620161290.099696.049696.0000000020160.288579221006.0P33282200000500011.021Northeast0